Glottal wave analysis with Pitch Synchronous Iterative Adaptive Inverse Filtering
Speech Communication - Eurospeech '91
Support vector machines: hype or hallelujah?
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Solving Multi-class Pattern Recognition Problems with Tree-Structured Support Vector Machines
Proceedings of the 23rd DAGM-Symposium on Pattern Recognition
The role of voice quality in communicating emotion, mood and attitude
Speech Communication - Special issue on speech and emotion
Fuzzy-input fuzzy-output one-against-all support vector machines
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
IEEE Transactions on Neural Networks
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The dynamic use of voice qualities in spoken language can reveal useful information on a speaker's attitude, mood and affective states. This information may be desirable for a range of speech technology applications. However, annotation of voice quality may frequently be inconsistent across raters. But whom should one trust or is the truth somewhere in between? The current study looks first to describe a voice quality feature set that is suitable for differentiating voice qualities on a tense to breathy dimension. These features are used as inputs to a fuzzy-input fuzzy-output support vector machine (F2SVM) algorithm, to automatically classify the voice qualities. The F2SVM is compared to standard approaches and shows promising results. Performances for cross validation, leave one speaker out, and cross corpus experiments of around 90% are achieved.